Knowledge extraction using neural network by an artificial life approach

Author(s):  
Yuji Makita ◽  
Masafurni Hagiwara
Author(s):  
Ryoji Sawa ◽  
◽  
Yuji Makita ◽  
Masafumi Hagiwara

Studies that target excellent information processing systems simulating life systems include the artificial-life (AL) incorporating emergence phenomenon - individual elements interacting based on lower level rules to generate higher level complex phenomena. Living things conserve species by replicating genes. We propose knowledge extraction promoting emergence by an AL approach incorporating living conservation of species and gene evolution. The proposed system consists of an AL environment and a knowledge extraction network. In the AL environment, individual elements interact and obtained data is input to the knowledge extraction network to present knowledge as a form of rules. Sets of rules are regarded as genes individual elements bequeath and new elements inherit these genes. We deal with a route-finding problem that, in simulation, sets a difficult situation whose goal is unknown, considering the actual world. Rules on route maps are extracted and extracted sets of rules are regarded as genes whose evolution is simulated through gene combination. We verified that the system finds routes effectively using evolved genes in a map made complex by combining maps.


2008 ◽  
Vol 2 (4) ◽  
Author(s):  
H. Md. Azamathulla ◽  
Aminuddin Ab Ghani ◽  
Nor Azazi Zakaria ◽  
Chang Chun Kiat ◽  
Leow Cheng Siang

2013 ◽  
Vol 507 ◽  
pp. 19-32 ◽  
Author(s):  
Line Kong-A-Siou ◽  
Kévin Cros ◽  
Anne Johannet ◽  
Valérie Borrell-Estupina ◽  
Séverin Pistre

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